Overview

Dataset statistics

Number of variables39
Number of observations260601
Missing cells0
Missing cells (%)0.0%
Duplicate rows9749
Duplicate rows (%)3.7%
Total size in memory87.6 MiB
Average record size in memory352.4 B

Variable types

Categorical31
Numeric8

Alerts

Dataset has 9749 (3.7%) duplicate rowsDuplicates
land_surface_condition is highly imbalanced (51.3%)Imbalance
foundation_type is highly imbalanced (60.9%)Imbalance
ground_floor_type is highly imbalanced (59.3%)Imbalance
position is highly imbalanced (50.4%)Imbalance
plan_configuration is highly imbalanced (90.7%)Imbalance
has_superstructure_adobe_mud is highly imbalanced (56.8%)Imbalance
has_superstructure_stone_flag is highly imbalanced (78.4%)Imbalance
has_superstructure_cement_mortar_stone is highly imbalanced (86.9%)Imbalance
has_superstructure_mud_mortar_brick is highly imbalanced (64.1%)Imbalance
has_superstructure_cement_mortar_brick is highly imbalanced (61.5%)Imbalance
has_superstructure_bamboo is highly imbalanced (58.0%)Imbalance
has_superstructure_rc_non_engineered is highly imbalanced (74.6%)Imbalance
has_superstructure_rc_engineered is highly imbalanced (88.2%)Imbalance
has_superstructure_other is highly imbalanced (88.8%)Imbalance
legal_ownership_status is highly imbalanced (86.0%)Imbalance
has_secondary_use_agriculture is highly imbalanced (65.5%)Imbalance
has_secondary_use_hotel is highly imbalanced (78.8%)Imbalance
has_secondary_use_rental is highly imbalanced (93.2%)Imbalance
has_secondary_use_institution is highly imbalanced (98.9%)Imbalance
has_secondary_use_school is highly imbalanced (99.5%)Imbalance
has_secondary_use_industry is highly imbalanced (98.8%)Imbalance
has_secondary_use_health_post is highly imbalanced (99.7%)Imbalance
has_secondary_use_gov_office is highly imbalanced (99.8%)Imbalance
has_secondary_use_use_police is highly imbalanced (99.9%)Imbalance
has_secondary_use_other is highly imbalanced (95.4%)Imbalance
geo_level_1_id has 4011 (1.5%) zerosZeros
age has 26041 (10.0%) zerosZeros
count_families has 20862 (8.0%) zerosZeros

Reproduction

Analysis started2024-04-12 10:29:08.981095
Analysis finished2024-04-12 10:29:18.956739
Duration9.98 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

damage_grade
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
2
148259 
3
87218 
1
25124 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Length

2024-04-12T12:29:18.980910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:19.012690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Most occurring characters

ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

geo_level_1_id
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.900353
Minimum0
Maximum30
Zeros4011
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2024-04-12T12:29:19.046456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median12
Q321
95-th percentile27
Maximum30
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.0336166
Coefficient of variation (CV)0.57794334
Kurtosis-1.2132488
Mean13.900353
Median Absolute Deviation (MAD)6
Skewness0.27253035
Sum3622446
Variance64.538996
MonotonicityNot monotonic
2024-04-12T12:29:19.086054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
6 24381
 
9.4%
26 22615
 
8.7%
10 22079
 
8.5%
17 21813
 
8.4%
8 19080
 
7.3%
7 18994
 
7.3%
20 17216
 
6.6%
21 14889
 
5.7%
4 14568
 
5.6%
27 12532
 
4.8%
Other values (21) 72434
27.8%
ValueCountFrequency (%)
0 4011
 
1.5%
1 2701
 
1.0%
2 931
 
0.4%
3 7540
 
2.9%
4 14568
5.6%
5 2690
 
1.0%
6 24381
9.4%
7 18994
7.3%
8 19080
7.3%
9 3958
 
1.5%
ValueCountFrequency (%)
30 2686
 
1.0%
29 396
 
0.2%
28 265
 
0.1%
27 12532
4.8%
26 22615
8.7%
25 5624
 
2.2%
24 1310
 
0.5%
23 1121
 
0.4%
22 6252
 
2.4%
21 14889
5.7%

geo_level_2_id
Real number (ℝ)

Distinct1414
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.07469
Minimum0
Maximum1427
Zeros38
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2024-04-12T12:29:19.126485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69
Q1350
median702
Q31050
95-th percentile1377
Maximum1427
Range1427
Interquartile range (IQR)700

Descriptive statistics

Standard deviation412.71073
Coefficient of variation (CV)0.58868298
Kurtosis-1.1882325
Mean701.07469
Median Absolute Deviation (MAD)349
Skewness0.028957381
Sum1.8270076 × 108
Variance170330.15
MonotonicityNot monotonic
2024-04-12T12:29:19.169671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 4038
 
1.5%
158 2520
 
1.0%
181 2080
 
0.8%
1387 2040
 
0.8%
157 1897
 
0.7%
363 1760
 
0.7%
463 1740
 
0.7%
673 1704
 
0.7%
533 1684
 
0.6%
883 1626
 
0.6%
Other values (1404) 239512
91.9%
ValueCountFrequency (%)
0 38
 
< 0.1%
1 204
0.1%
3 77
 
< 0.1%
4 315
0.1%
5 25
 
< 0.1%
6 2
 
< 0.1%
7 100
 
< 0.1%
8 120
 
< 0.1%
9 333
0.1%
10 354
0.1%
ValueCountFrequency (%)
1427 6
 
< 0.1%
1426 286
0.1%
1425 466
0.2%
1424 7
 
< 0.1%
1423 3
 
< 0.1%
1422 216
0.1%
1421 254
0.1%
1420 10
 
< 0.1%
1419 95
 
< 0.1%
1418 152
 
0.1%

geo_level_3_id
Real number (ℝ)

Distinct11595
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6257.8761
Minimum0
Maximum12567
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2024-04-12T12:29:19.215299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile611
Q13073
median6270
Q39412
95-th percentile11927
Maximum12567
Range12567
Interquartile range (IQR)6339

Descriptive statistics

Standard deviation3646.3696
Coefficient of variation (CV)0.58268485
Kurtosis-1.2138965
Mean6257.8761
Median Absolute Deviation (MAD)3171
Skewness0.00039351209
Sum1.6308088 × 109
Variance13296012
MonotonicityNot monotonic
2024-04-12T12:29:19.261713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
633 651
 
0.2%
9133 647
 
0.2%
621 530
 
0.2%
11246 470
 
0.2%
2005 466
 
0.2%
11440 455
 
0.2%
7723 443
 
0.2%
9229 381
 
0.1%
2452 349
 
0.1%
12258 312
 
0.1%
Other values (11585) 255897
98.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 6
 
< 0.1%
3 9
 
< 0.1%
5 14
 
< 0.1%
6 21
 
< 0.1%
7 2
 
< 0.1%
8 31
< 0.1%
9 3
 
< 0.1%
10 1
 
< 0.1%
11 62
< 0.1%
ValueCountFrequency (%)
12567 1
 
< 0.1%
12565 7
 
< 0.1%
12564 6
 
< 0.1%
12563 24
< 0.1%
12562 3
 
< 0.1%
12561 19
< 0.1%
12560 17
 
< 0.1%
12559 6
 
< 0.1%
12558 6
 
< 0.1%
12557 44
< 0.1%

count_floors_pre_eq
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1297232
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2024-04-12T12:29:19.297598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72766455
Coefficient of variation (CV)0.34167095
Kurtosis2.3225979
Mean2.1297232
Median Absolute Deviation (MAD)0
Skewness0.83411296
Sum555008
Variance0.52949569
MonotonicityNot monotonic
2024-04-12T12:29:19.332539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 156623
60.1%
3 55617
 
21.3%
1 40441
 
15.5%
4 5424
 
2.1%
5 2246
 
0.9%
6 209
 
0.1%
7 39
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 40441
 
15.5%
2 156623
60.1%
3 55617
 
21.3%
4 5424
 
2.1%
5 2246
 
0.9%
6 209
 
0.1%
7 39
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1
 
< 0.1%
7 39
 
< 0.1%
6 209
 
0.1%
5 2246
 
0.9%
4 5424
 
2.1%
3 55617
 
21.3%
2 156623
60.1%
1 40441
 
15.5%

age
Real number (ℝ)

ZEROS 

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.535029
Minimum0
Maximum995
Zeros26041
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2024-04-12T12:29:19.374024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median15
Q330
95-th percentile60
Maximum995
Range995
Interquartile range (IQR)20

Descriptive statistics

Standard deviation73.565937
Coefficient of variation (CV)2.7724084
Kurtosis157.24824
Mean26.535029
Median Absolute Deviation (MAD)10
Skewness12.192494
Sum6915055
Variance5411.947
MonotonicityNot monotonic
2024-04-12T12:29:19.416819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
10 38896
14.9%
15 36010
13.8%
5 33697
12.9%
20 32182
12.3%
0 26041
10.0%
25 24366
9.3%
30 18028
6.9%
35 10710
 
4.1%
40 10559
 
4.1%
50 7257
 
2.8%
Other values (32) 22855
8.8%
ValueCountFrequency (%)
0 26041
10.0%
5 33697
12.9%
10 38896
14.9%
15 36010
13.8%
20 32182
12.3%
25 24366
9.3%
30 18028
6.9%
35 10710
 
4.1%
40 10559
 
4.1%
45 4711
 
1.8%
ValueCountFrequency (%)
995 1390
0.5%
200 106
 
< 0.1%
195 2
 
< 0.1%
190 3
 
< 0.1%
185 1
 
< 0.1%
180 7
 
< 0.1%
175 5
 
< 0.1%
170 6
 
< 0.1%
165 2
 
< 0.1%
160 6
 
< 0.1%

area_percentage
Real number (ℝ)

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0180506
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2024-04-12T12:29:19.459656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q39
95-th percentile16
Maximum100
Range99
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.3922309
Coefficient of variation (CV)0.54779287
Kurtosis30.438258
Mean8.0180506
Median Absolute Deviation (MAD)2
Skewness3.5260823
Sum2089512
Variance19.291693
MonotonicityNot monotonic
2024-04-12T12:29:19.504509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 42013
16.1%
7 36752
14.1%
5 32724
12.6%
8 28445
10.9%
9 22199
8.5%
4 19236
7.4%
10 15613
 
6.0%
11 13907
 
5.3%
3 11837
 
4.5%
12 7581
 
2.9%
Other values (74) 30294
11.6%
ValueCountFrequency (%)
1 90
 
< 0.1%
2 3181
 
1.2%
3 11837
 
4.5%
4 19236
7.4%
5 32724
12.6%
6 42013
16.1%
7 36752
14.1%
8 28445
10.9%
9 22199
8.5%
10 15613
 
6.0%
ValueCountFrequency (%)
100 1
 
< 0.1%
96 3
< 0.1%
90 1
 
< 0.1%
86 5
< 0.1%
85 4
< 0.1%
84 3
< 0.1%
83 3
< 0.1%
82 1
 
< 0.1%
80 1
 
< 0.1%
78 1
 
< 0.1%

height_percentage
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4343652
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2024-04-12T12:29:19.542945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile9
Maximum32
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9184182
Coefficient of variation (CV)0.35301607
Kurtosis14.318526
Mean5.4343652
Median Absolute Deviation (MAD)1
Skewness1.8082618
Sum1416201
Variance3.6803285
MonotonicityNot monotonic
2024-04-12T12:29:19.577688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5 78513
30.1%
6 46477
17.8%
4 37763
14.5%
7 35465
13.6%
3 25957
 
10.0%
8 13902
 
5.3%
2 9305
 
3.6%
9 5376
 
2.1%
10 4492
 
1.7%
11 917
 
0.4%
Other values (17) 2434
 
0.9%
ValueCountFrequency (%)
2 9305
 
3.6%
3 25957
 
10.0%
4 37763
14.5%
5 78513
30.1%
6 46477
17.8%
7 35465
13.6%
8 13902
 
5.3%
9 5376
 
2.1%
10 4492
 
1.7%
11 917
 
0.4%
ValueCountFrequency (%)
32 75
< 0.1%
31 1
 
< 0.1%
28 2
 
< 0.1%
26 2
 
< 0.1%
25 3
 
< 0.1%
24 4
 
< 0.1%
23 11
 
< 0.1%
21 13
 
< 0.1%
20 33
< 0.1%
19 7
 
< 0.1%

land_surface_condition
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
t
216757 
n
35528 
o
 
8316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowo
3rd rowt
4th rowt
5th rowt

Common Values

ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

Length

2024-04-12T12:29:19.614454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:19.644027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

Most occurring characters

ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

foundation_type
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
r
219196 
w
 
15118
u
 
14260
i
 
10579
h
 
1448

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowr
2nd rowr
3rd rowr
4th rowr
5th rowr

Common Values

ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

Length

2024-04-12T12:29:19.677140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:19.708052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

Most occurring characters

ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

roof_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
n
182842 
q
61576 
x
 
16183

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rown
2nd rown
3rd rown
4th rown
5th rown

Common Values

ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

Length

2024-04-12T12:29:19.741569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:19.772342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

Most occurring characters

ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

ground_floor_type
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
f
209619 
x
24877 
v
24593 
z
 
1004
m
 
508

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd rowx
3rd rowf
4th rowf
5th rowf

Common Values

ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

Length

2024-04-12T12:29:19.804544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:19.836882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

Most occurring characters

ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

other_floor_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
q
165282 
x
43448 
j
39843 
s
 
12028

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowq
2nd rowq
3rd rowx
4th rowx
5th rowx

Common Values

ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

Length

2024-04-12T12:29:19.872839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:19.903525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

Most occurring characters

ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

position
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
s
202090 
t
42896 
j
 
13282
o
 
2333

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rows
3rd rowt
4th rows
5th rows

Common Values

ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

Length

2024-04-12T12:29:19.938486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:19.969711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

Most occurring characters

ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

plan_configuration
Categorical

IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
d
250072 
q
 
5692
u
 
3649
s
 
346
c
 
325
Other values (5)
 
517

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd
2nd rowd
3rd rowd
4th rowd
5th rowd

Common Values

ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

Length

2024-04-12T12:29:20.004728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.040072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

has_superstructure_adobe_mud
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
237500 
1
 
23101

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%

Length

2024-04-12T12:29:20.080034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.108127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
1
198561 
0
62040 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

Length

2024-04-12T12:29:20.138355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.168358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

Most occurring characters

ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

has_superstructure_stone_flag
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
251654 
1
 
8947

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%

Length

2024-04-12T12:29:20.199639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.228042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
255849 
1
 
4752

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%

Length

2024-04-12T12:29:20.259359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.287138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
242840 
1
 
17761

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%

Length

2024-04-12T12:29:20.317373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.345008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
240986 
1
 
19615

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%

Length

2024-04-12T12:29:20.375634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.405097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
194151 
1
66450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

Length

2024-04-12T12:29:20.435422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.464050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

Most occurring characters

ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

has_superstructure_bamboo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
238447 
1
 
22154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%

Length

2024-04-12T12:29:20.495365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.523208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
249502 
1
 
11099

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

Length

2024-04-12T12:29:20.554531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.583205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

has_superstructure_rc_engineered
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
256468 
1
 
4133

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

Length

2024-04-12T12:29:20.613520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.643085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

has_superstructure_other
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
256696 
1
 
3905

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

Length

2024-04-12T12:29:20.673444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.701408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

legal_ownership_status
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
v
250939 
a
 
5512
w
 
2677
r
 
1473

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowv
2nd rowv
3rd rowv
4th rowv
5th rowv

Common Values

ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

Length

2024-04-12T12:29:20.731732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.761365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

Most occurring characters

ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

count_families
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98394864
Minimum0
Maximum9
Zeros20862
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size12.0 MiB
2024-04-12T12:29:20.792061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41838898
Coefficient of variation (CV)0.42521424
Kurtosis17.670943
Mean0.98394864
Median Absolute Deviation (MAD)0
Skewness1.6347579
Sum256418
Variance0.17504934
MonotonicityNot monotonic
2024-04-12T12:29:20.822594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 226115
86.8%
0 20862
 
8.0%
2 11294
 
4.3%
3 1802
 
0.7%
4 389
 
0.1%
5 104
 
< 0.1%
6 22
 
< 0.1%
7 7
 
< 0.1%
9 4
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 20862
 
8.0%
1 226115
86.8%
2 11294
 
4.3%
3 1802
 
0.7%
4 389
 
0.1%
5 104
 
< 0.1%
6 22
 
< 0.1%
7 7
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
9 4
 
< 0.1%
8 2
 
< 0.1%
7 7
 
< 0.1%
6 22
 
< 0.1%
5 104
 
< 0.1%
4 389
 
0.1%
3 1802
 
0.7%
2 11294
 
4.3%
1 226115
86.8%
0 20862
 
8.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
231445 
1
29156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

Length

2024-04-12T12:29:20.856106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.885800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

has_secondary_use_agriculture
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
243824 
1
 
16777

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

Length

2024-04-12T12:29:20.916218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:20.945134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

has_secondary_use_hotel
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
251838 
1
 
8763

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

Length

2024-04-12T12:29:20.975213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:21.003051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

has_secondary_use_rental
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
258490 
1
 
2111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

Length

2024-04-12T12:29:21.033432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:21.061519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

has_secondary_use_institution
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
260356 
1
 
245

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

Length

2024-04-12T12:29:21.092308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:21.121568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

has_secondary_use_school
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
260507 
1
 
94

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

Length

2024-04-12T12:29:21.151867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:21.181670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

has_secondary_use_industry
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
260322 
1
 
279

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

Length

2024-04-12T12:29:21.212096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:21.240406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

has_secondary_use_health_post
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
260552 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

Length

2024-04-12T12:29:21.271839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:21.299755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

has_secondary_use_gov_office
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
260563 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

Length

2024-04-12T12:29:21.329368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:21.357718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

has_secondary_use_use_police
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
260578 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

Length

2024-04-12T12:29:21.388252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:21.417390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

has_secondary_use_other
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
0
259267 
1
 
1334

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Length

2024-04-12T12:29:21.447642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T12:29:21.476052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Interactions

2024-04-12T12:29:17.331002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:14.825395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.162989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.490984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.820780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.146571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.461398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.011739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.367955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:14.874764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.208642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.531984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.861481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.186336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.735797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.050899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.407368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:14.921101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.248919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.575799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.903006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.226866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.776775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.091010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.444658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:14.962015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.289139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.616965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.944809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.265615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.818065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.134801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.484925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.003774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.331586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.661463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.987183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.308286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.859054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.176775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.522702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.046937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.372307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.701632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.027186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.345643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.897638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.216624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.558305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.086986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.412190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.741949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.067380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.384976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.934836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.255789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.596397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.126212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.452506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:15.783621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.107259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.424538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:16.973547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T12:29:17.294414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-12T12:29:17.685313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-12T12:29:18.135557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

damage_gradegeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_other
building_id
802906364871219823065trnfqtd11000000000v100000000000
2883028900281221087ornxqsd01000000000v100000000000
94947321363897321055trnfxtd01000000000v100000000000
5908822224181069421065trnfxsd01000011000v100000000000
201944311131148833089trnfxsd10000000000v100000000000
33302028558608921095trnfqsd01000000000v111000000000
728451394751206622534nrnxqsd01000000000v100000000000
475515120323122362086twqvxsu00000110000v100000000000
44112620757721921586trqfqsd01000010000v100000000000
98950012688699410134tinvjsd00000100000v100000000000
damage_gradegeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_other
building_id
560805320368598012553nrnfjsd01000000000v111000000000
2076832101382190322555trnfqsd01000010000v100000000000
22642128767861325135trnfqsd01000000000v111000000000
1595552271811537601312trnfxjd00001000000v100000000000
82701238268471822085trnfqsd01000000000v100000000000
6886362251335162115563nrnfjsq01000000000v100000000000
66948531771520602065trnfqsd01000000000v100000000000
60251231751816335567trqfqsd01000000000v100000000000
151409226391851210146trxvsjd00000100000v100000000000
7475943219910131076nrnfqjd01000000000v300000000000

Duplicate rows

Most frequently occurring

damage_gradegeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_other# duplicates
767831013826532568trnfqsd01000000000v10000000000029
8494317930898432067trnfqsd01000011000v10000000000015
95603272691112121587trnfxsd01000000000v10000000000015
4649220863857821097trqxxsd01000010000v10000000000014
35912639112461063tuqvjsq00000100000a10000000000012
14532412181053121065trnfqsd01000000000v10000000000012
287621070942921054trnfqsd01000000000v10000000000012
956432726911121215107trnfxsd01000000000v10000000000012
7030381114800221565nrnfqsd01000000000v10000000000011
4122216244212422065trnfxsd01000000000v10000000000010